Transcript 2007-Arizona-State-Grand-Rounds-Controlled Terminologies in

Controlled Terminologies in
Patient Care and Research:
An Informatics Perspective
James J. Cimino, M.D.
Department of Biomedical Informatics
Columbia University
Overview
• Motivation for data encoding: reuse
• Challenges to encoding with controlled terminologies
• Approach at Columbia/NY Presbyterian Hospital
• Desiderata for controlled terminologies
• Successful data reuse at Columbia/NYPH
Problems We Are Trying to Solve
• Collecting data from disparate sources
• Aggregating like data
• Sharing data
• Reusing data
– Patient care
– Administrative functions
– Research
– Automated decision support
Information Form and Reuse
Information Form and Reuse
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21 22 23 24 25 26 27 28 29
Patient
Care Data
Research
Data
Text
Images
Text
Processing
?
Finds what is mentioned
but not what is discussed
(ambiguity, redundancy,
false positives, false
negatives)
Patient
Care Data
Research
Data
Text
Images
Natural
Language
Processing
Feature
Extraction
Controlled terminology;
distinguishes what is
discussed from what is
mentioned (concept
oriented)
Patient
Care Data
Text
Research
Data
Images
Encoded
Data
Controlled
Terminologies
Gender
Causes of
Death
Knowledge
Data
Mining
Symbolic
Manipulation
Knowledge
Networks
Reuse
Patient Care
Case Presentation
The patient is a 50 year old female who presents to the emergency room with the chief
complaint of cough and chest pain. The patient reports that she has had a productive
cough for three days but that chest pain developed one hour ago.
She reports that she was treated in the past for tuberculosis while she was pregnant,
and that she is allergic to Bufferin.
Physical examination reveals a well-developed, well-nourished female in moderate
respiratory distress. Vital signs showed a pulse of 90, a respiratory rate of 22, an oral
temperature of 101.3, and a blood pressure of 150/100. Examination reveals rales and
rhonchi in the left upper chest.
Labs:
Chem7 (serum): Glucose 100
Chem7 (plasma): Glucose 150
CBC: Hgb 15, Hct 45, WBC 11,000
A fingerstick blood sugar was 80
Urinalysis showed protein of 1+ and glucose of 0
Chest X-ray: Left upper lobe infiltrate, left ventricular hypertrophy
The patient is started on antibiotics and aspirin and is admitted to the hospital.
A medical student reviewing the case is concerned about patients with pneumonia and
myocardial infarction. She decides to do a literature search.
The ER physician is wondering if this patient could be heralding an epidemic.
Reuse of Clinical Data
a) To what bed should the patient be admitted?
b) What were all the results of the patient's blood glucose
tests (including serum, plasma and fingerstick)?
c) Does the patient have a history of tuberculosis?
d) Is the patient allergic to any ordered medications?
e) How often are patient with the diagnosis of myocardial
infarction started on beta blockers?
f)
Can the patient’s data be used by an expert system?
g) Can the patient’s data be used to search health literature?
h) Does the patient represent an index case in an epidemic?
i)
Does the patient meet the criteria for a clinical trial of
patients over the age of 50 with elevated blood pressure?
To what bed should the patient be admitted?
“Patient is an 50 year old female…”
Electronic
Medical
Record
Admission
Discharge
Transfer
System
“Put the patient in
Room 5, Bed B…”
To what bed should the patient be admitted?
But: how does the computer know the
patient is female?
The record could say:
“female”
“Female”
“FEMALE”
“F”
“Woman”
“Girl”
Coding the Data: Gender
• Data element - gender
• Controlled terminology: Male,
Female, Unknown
• Representation: M,F,U; 0,1,2
• What about other values?
What’s the Gender?
What are the blood glucose test results?
Does the patient have a history of tuberculosis?
420 ICD9-CM Tuberculosis Codes
(plus 69 hierarchical codes)
010.
010.0
010.00
010.01
010.02
010.03
010.04
010.05
010.06
010.1
010.8
010.9
PRIMARY TB INFECTION*
PRIMARY TB COMPLEX*
PRIM TB COMPLEX-UNSPEC
PRIM TB COMPLEX-NO EXAM
PRIM TB COMPLEX-EXM UNKN
PRIM TB COMPLEX-MICRO DX
PRIM TB COMPLEX-CULT DX
PRIM TB COMPLEX-HISTO DX
PRIM TB COMPLEX-OTH TEST
PRIMARY TB PLEURISY*
PRIM PROGRESSIVE TB NEC*
PRIMARY TB INFECTION NOS*
011.
012.
013.
014.
015.
016.
017.
018.
PULMONARY TUBERCULOSIS*
OTHER RESPIRATORY TB*
CNS TUBERCULOSIS*
INTESTINAL TB*
TB OF BONE AND JOINT*
GENITOURINARY TB*
TUBERCULOSIS NEC*
MILIARY TUBERCULOSIS*
Does the patient have a history of tuberculosis?
Thirteen TB codes not under 01x.
137.
137.0
137.1
137.2
137.3
137.4
647.
647.3
647.30
647.31
647.32
647.33
647.34
LATE EFFECT TUBERCULOSIS*
LATE EFFECT TB, RESP/NOS
LATE EFFECT CNS TB
LATE EFFECT GU TB
LATE EFF BONE & JOINT TB
LATE EFFECT TB NEC
INFECTIVE DIS IN PREG*
TUBERCULOSIS IN PREG*
TB IN PREG-UNSPECIFIED
TUBERCULOSIS-DELIVERED
TUBERCULOSIS-DELIV W P/P
TUBERCULOSIS-ANTEPARTUM
TUBERCULOSIS-POSTPARTUM
New York Presbyterian Hospital
Clinical Information Systems Architecture
Medical Logic
Modules
Clinical Database
Alerts & Reminders
Database Monitor
Results Review
Database
Interface
Medical Entities
Dictionary
Administrative
Research
Reformatter
...
Radiology
Reformatter
Discharge
Summaries
Reformatter
Laboratory
...
Medical Entities Dictionary:
A Central Terminology Repository
Communicating Terminology Changes
K#1 = 4.2
K#1 = 3.3
K#2 = 3.2
K#1 = 3.0
K#3 = 2.6
K#1
K#2 K#3
Patient
Care Data
Text
Research
Data
Knowledge
Data
Mining
Images
Encoded
Data
Symbolic
Manipulation
Causes of
Death
Reuse
Patient Care
Controlled
Terminologies
Gender
Knowledge
Networks
Quality
Control
Desiderata
Terminology Desiderata
Cimino JJ. Desiderata for controlled medical vocabularies in
the Twenty-First Century. Methods of Information in
Medicine; 1998;37(4-5):394-403.
•
•
•
•
•
•
Concept orientation
Concept permanence
Nonsemantic identifiers
Polyhierarchy
Reject “Not Elsewhere Classified”
Formal definitions
Polyhierarchy
disease
infectious disease
cholera
lung disease
meningitis
tuberculosis
infectious disease
in pregnancy
tuberculosis in pregnancy
Communication with Hierarchies
K#1 = 4.2
K#1 = 3.3
K#2 = 3.2
K#1 = 3.0
K#3 = 2.6
K#1
K#2 K#3
Communication with Hierarchies
K#1 = 4.2
K#1 = 3.3
K#2 = 3.2
K#1 = 3.0
K#3 = 2.6
K
K#1
K#2 K#3
Reject “Not Elsewhere Classified”
1995
Diagnosis ICD9-CM
Code
1996
ICD9-CM
Name
Diagnosis ICD9-CM
Code
ICD9-CM
Name
Hepatitis A
070.1
Hepatitis A
Hepatitis A
070.1
Hepatitis A
Hepatitis B
070.3
Hepatitis B
Hepatitis B
070.3
Hepatitis B
Hepatitis C
070.5
Hepatitis NEC
Hepatitis C
070.4
Hepatitis C
Hepatitis E
070.5
Hepatitis NEC
Hepatitis E
070.5
Hepatitis NEC
Viral Hepatitis Mortality
The “Will Rogers Phenomenon”:
During the Great Dust Bowl Era,
when Oakies moved to California,
the IQ in both states increased.
070.1
070.3
070.5
1994
1995
1996
Formal Definitions in the MED
Medical
Entity
Substance
Chemical
Laboratory
Specimen
Anatomic
Substance
Plasma
Carbohydrate
Bioactive
Substance
Glucose
Plasma
Specimen
Event
Diagnostic
Procedure
Laboratory
Test
Plasma
Glucose
Test
Laboratory
Procedure
CHEM-7
Part of
MED Data Model
MED Code
1600
1600
1600
1600
Nonsemantic 1600
Identifier
1600
1600
1600
1724
31987
32703
50000
Slot Code
Value
4 Polyhier- 32703, 50000
6
archy "Serum Glucose Measurement"
8
1724
Formal
16
31987
Concept
Definitions
18
"mg/dl"
Oriented
39
"50"
Concept
40
"110"
Permanence
212
"2345-7"
6
"SMAC"
6
"Glucose"
6
"Serum Glucose Tests“
6
"CPMC Lab Test "
Slot
4
6
8
16
18
39
40
212
Slot Name
SUBCLASS-OF
PRINT-NAME
PART-OF
SUBSTANCE-MEASURED
UNITS
LOW-NORMAL-VALUE
HIGH-NORMAL-VALUE
LOINC-CODE
Using the MED
WebCIS
MED
QueryMED
Decision
Support
Translation
Table
Interface
Engine
The MED and Messaging
Clinical
Data
Repository
Ancillary
System
Local
Codes
Interface Engine
Translation
Table
MED
Codes
Other
Subscribers
Using the MED
• Translation
– What is the display name for …?
– What is the ICD9 Code for …?
– What is the aggregation class for …?
• Translation Tables
• Class-based questions
– Is Piroxicam a nonsteroidal antiinflammatory drug?
– What are all the antibiotics?
• Knowledge queries
– What are the pharmaceutic ingredients of…?
What’s in the MED?
• Sunquest lab terms
• Cerner lab terms
• Digimedix drugs
• Cerner Drugs
• Sunquest Radiology
• ICD9-based problem list terms
• Eclipsys order catalogue
• Other applications
• Knowledge terms
The MED Today
•
•
•
•
•
•
“Concept”-based (102,071)
Multiple hierarchy (152,508)
Synonyms (883,095)
Translations (436,005)
Semantic links (395,854)
Attributes (2,030,184)
What are the blood glucose test results?
What are the blood glucose test results?
Using the MED for Summary Reporting
Lab Display
Lab Test
Intravascular Glucose Test
Fingerstick Glucose Test
Serum Glucose Test
Plasma Glucose Test
Chem20 Display
What are the blood glucose test results?
DOP Summary
What are the blood glucose test results?
WebCIS Summary
What are the blood glucose test results?
Eclipsys Summary
Adapting to Changing Requirements
•
•
•
•
•
Labs ordered as panels of tests
HCFA will only reimburse for tests
Clinicians have to order tests separately
But: they want to review them as panels
Changing the architecture:
– Order tests separately
– Group them for display
– 2 FTEs
– 4 months of work
• Solution: 5 minute change in the MED
Lab Tests and Procedures in the MED
Lab Procedures
Chem7
SMAC
Lab Tests
CBC
Sodium
Glucose
Hematocrit
Lab Tests and Procedures in the MED
Lab Procedures
Chem7
SMAC
Lab Tests
CBC
Orderable
Tests
Sodium
Glucose
Hematocrit
Is the patient allergic to any ordered medications?
1) Check the drugs’ allergy codes, or…
2) Infer the allergy codes from the MED, or…
3) Use formal definitions in the MED to check ingredients
Allergy: Bufferin
Ordered Medications: Enteric-Coated Aspirin
If ingredient of allergic drug equals ingredient of ordered drug,
then send alert
Aspirin Preparations
Bufferin
has-ingredient
Enteric-Coated Aspirin
Aspirin
Does the patient have a history of tuberculosis?
Tuberculosis
Infection
Primary
TB (010)
Primary TB
Complex 010.0
Primary TB
Complex
Uspec
010.00
Pulmonary
TB (011)
Other Resp
TB (012)
Infective Disease
in Pregnancy (647)
Late Effect
TB (137)
Primary TB
Pleurisy 010.1
Primary TB
Complex
No Exam
010.01
Primary TB
Pleurisy
Uspec
010.10
Primary TB
Pleurisy
No Exam
010.11
TB in
Preg (647.3)
How often are patient with the diagnosis of
myocardial infarction started on beta blockers?
How often are patient with the diagnosis of
myocardial infarction started on beta blockers?
select patient_id , time = primary_time
from visit2004_diagnosis
where diagnosis_code = 2618
and b.primary_time between '01/01/2000' and '01/01/2005'
and b.comp_code = 28144
MI
MI+Beta
2000
2001
2002
2003
2004
Can the patient’s data be used by an expert
system?
Serum Potassium Test
Serum Specimen
Abnormalities of
Serum Potassium
Serum
Potassium
Hypokalemia
Can the patient’s data be used by an expert
system?
Can the patient’s data be used by an expert
system?
Can the patient’s data be used by an expert
system?
Can the patient’s data be used to search health
literature?
Injectable
Gentamicin
Serum
Gentamicin
Level
Gentamicin
Gentamicn
Sensitivity
Test
Lab
Manual
Drug
Information
Gentamicin
Toxicity
PubMed
Expert
System
Reuse of Clinical Data
Patient
Care Data
Text
Research
Data
Knowledge
Data
Mining
Images
Encoded
Data
Symbolic
Manipulation
Causes of
Death
Reuse
Patient Care
Controlled
Terminologies
Gender
Knowledge
Networks
Quality
Control
Desiderata
Reuse of Clinical Data
a) To what bed should the patient be admitted?
b) What were all the results of the patient's blood glucose
tests (including serum, plasma and fingerstick)?
c) Does the patient have a history of tuberculosis?
d) Is the patient allergic to any ordered medications?
e) How often are patient with the diagnosis of myocardial
infarction started on beta blockers?
f)
Can the patient’s data be used by an expert system?
g) Can the patient’s data be used to search health literature?
h) Does the patient represent an index case in an epidemic?
i)
Does the patient meet the criteria for a clinical trial of
patients over the age of 50 with elevated blood pressure?
Conclusions
Terminology is key to data integration and reuse
High-quality terminology supports high-quality
data integration and reuse
“Desiderata” facilitate high quality
Columbia/NYPH Medical Entities Dictionary
Serves as a repository for institutional and standard
terminologies
Uses multihierarchy semantic network
Supports sophisticated data integration
Supports sophisticated data reuse
Questions
?